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Company: Enterprise Products
Location: Houston, TX
Career Level: Associate
Industries: Energy, Utilities, Environmental

Description

Enterprise Products Partners L.P. is one of the largest publicly traded partnerships and a leading North American provider of midstream energy services to producers and consumers of natural gas, NGLs, crude oil, refined products and petrochemicals. Our services include natural gas gathering, treating, processing, transportation and storage; NGL transportation, fractionation, storage and import and export terminals; crude oil gathering, transportation, storage and terminals; petrochemical and refined products transportation, storage and terminals; and a marine transportation business that operates primarily on the United States inland and Intracoastal Waterway systems. The partnership's assets include approximately 50,000 miles of pipelines; 260 million barrels of storage capacity for NGLs, crude oil, refined products and petrochemicals; and 14 billion cubic feet of natural gas storage capacity.   

The Predictive Maintenance Analyst is responsible for monitoring and diagnosing equipment performance using advanced predictive analytics software. This role supports operational reliability by identifying emerging equipment issues before failure, triaging anomalies, and engaging subject matter experts across corporate and field teams to resolve reliability concerns. This position offers a unique opportunity to apply analytical and collaboration skills within a complex midstream oil and gas environment, directly contributing to improved equipment uptime, reliability, and performance.

Responsibilities include, but are not limited to:

  •   Monitor and analyze plant equipment performance using predictive analytics platforms such as Seeq, GE Vernova, and Aspen Mtell.

  •   Identify and diagnose anomalies, trends, and patterns in operational data to assess equipment health.

  •   Evaluate and triage predictive alerts, documenting probable causes, severity, and recommended next steps.

  •   Communicate and collaborate with corporate and field subject matter experts (SMEs) and field operations personnel to address identified reliability ussies.

  •   Develop and maintain dashboards and reports to track equipment condition trends and predictive analytics performance. 

  •   Contribute to the continuous improvement of predictive maintenance methodologies, data quality, and anomaly detection strategies.

  •   Support digital transformation initiatives by integrating predictive maintenance insights into reliability and operational workflows.

  •   Ensure consistent documentation and communication of predictive findings to enhance organizational learning and asset reliability.

 



Requirements

The successful candidate will meet the following qualifications:

  • 5–10 years of experience in equipment condition monitoring, reliability, or predictive maintenance within oil & gas or similar heavy industrial environments.

     

  • Strong knowledge of rotating equipment such as centrifugal and reciprocating compressors, pumps, internal combustion engines, gas turbines, and motors.

  • Working knowledge of process and instrumentation systems and how they relate to equipment performance.

  • Hands-on experience with predictive analytics or asset performance monitoring software (Seeq, GE Vernova, Aspen Mtell preferred).

  • Analytical mindset with the ability to translate data trends into actionable reliability insights.

  • Excellent communication and documentation skills; able to engage with both technical and operational stakeholders.

  • Knowledge of plant operations and maintenance.

  • Mechanical, process controls, or technical education background preferred. (degree not required).

  • Preferred certifications:

    • Certified Maintenance & Reliability Professional (CMRP)

    • Vibration Analysis (ISO 18436-2 Category 1 or higher)

    • Certified Reliability Analyst or Predictive Maintenance Specialist

    • Other relevant condition monitoring or reliability-based credentials

       

 


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